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I study how the quality of information affects politician selection in a two-candidate model where voters want to vote for the best candidate but also for the winner. Voters receive private and public signals about candidates’ relative valence. Public information has a stronger effect on equilibrium outcomes because voters use it to infer other voters’ beliefs. Contrary to what might be expected, more precise public information does not always benefit the better candidate’s electoral prospects: when voters’ private information is precise enough, improving public information hurts the better candidate’s electoral prospects. The model provides a rationale for the prevalence of large swings in voter sentiment in close elections, and for front-runner candidates’ tendency to avoid face-to-face television debates with the underdog.

This paper presents a new approach for studying temporal sequences across ordinal variables. It involves three complementary approaches (frequency tables, transitional graphs, and dependency tables), as well as an established adaptation based on Bayesian dynamical systems, inferring a general system of change. The frequency tables count pairs of values in two variables and transitional graphs depict changes, showing which variable tends to attain high values first. The dependency tables investigate which values of one variable are prerequisites for values in another, as a more direct test of causal hypotheses. We illustrate the proposed approaches by analyzing the V-Dem dataset, and show that changes in electoral democracy are preceded by changes in freedom of expression and access to alternative information.

Textual data are plagued by underreporting bias. For example, news sources often fail to report human rights violations. Cook et al. propose a multi-source estimator to gauge, and to account for, the underreporting of state repression events within human codings of news texts produced by the Agence France-Presse and Associated Press. We evaluate this estimator with Monte Carlo experiments, and then use it to compare the prevalence and seriousness of underreporting when comparable texts are machine coded and recorded in the World-Integrated Crisis Early Warning System dataset. We replicate Cook et al.’s investigation of human-coded state repression events with our machine-coded events, and validate both models against an external measure of human rights protections in Africa. We then use the Cook et al. estimator to gauge the seriousness and prevalence of underreporting in machine and human-coded event data on human rights violations in Colombia. We find in both applications that machine-coded data are as valid as human-coded data.

When and why do electoral commitments enhance parties’ ability to implement their preferred policy in multiparty governments? We propose an audience costs theory whereby strong platform commitments enhance parties’ negotiating positions in multiparty cabinets but only when they are on a salient policy issue for core voters and the party controls the policy-relevant portfolio. Utilizing new data on portfolio allocations in eight parliamentary democracies over 40 years, we show that absent a strong platform commitment, control of the portfolio of social affairs by social democrats, alone, is not associated with changes in welfare generosity. Notably, our findings are independent of party size and hold in most multiparty legislative systems not dominated by three parties.

Do minority voters respond to co-racial or co-ethnic candidates? That is does the increased chance of substantive representation translate into increased participation? Here, we focus on this question among African American voters. While much of the empirical literature on this question has produced conflicting answers, recent studies suggest that minority candidates can significantly increase minority turnout. We argue that past work on this topic does not adequately account for the fact that minority voters in places with minority candidates may systematically differ in their level of participation than minority voters in places without minority candidates. In this study we address the weaknesses of previous research designs and offer a new design that exploits the redistricting process to gain additional leverage over this question. We find little evidence that African American voter turnout increases when voters are moved to African American candidates. We find some evidence that white voters, however, tend to vote at lower rates when they are represented by African American candidates.

How does media influence beliefs, attitudes, and behaviors? While many scholars have studied the effect of media on social and political outcomes, we know surprisingly little about the channels through which this effect operates. I argue that two mechanisms can account for its impact. Media provides new information that persuades individuals to accept it (individual channel), but also, media informs listeners about what others learn, thus facilitating coordination (social channel). Combining a field experiment with a plausibly natural experiment in Mexico, I disentangle these effects analyzing norms surrounding violence against women. I examine the effect of a radio program when it is transmitted privately versus when it is transmitted publicly. I find no evidence supporting the individual mechanism. The social channel, however, increased rejection of violence against women and increased support for gender equality, but unexpectedly increased pessimism about whether violence would decline in the future.

We argue that democratic institutions influence property rights in attracting foreign direct investment (FDI) by providing: (1) a coherent logic to the property rights regime that is created in a state and (2) a legitimate way to manage conflicts that arise in dynamic economies. We expect that the marginal effect of property rights in attracting FDI has increased over time with the rate of technological dynamism. We test this using a non-nested multilevel modeling strategy with random coefficients on data from 1970 to 2009. Our results demonstrate that the effect of property rights on attracting FDI is contingent on democratic institutions and that this effect becomes more pronounced over time. This effect holds for both developing and developed countries across all regions.

Cluster-robust standard errors (as implemented by the eponymous cluster option in Stata) can produce misleading inferences when the number of clusters G is small, even if the model is consistent and there are many observations in each cluster. Nevertheless, political scientists commonly employ this method in data sets with few clusters. The contributions of this paper are: (a) developing new and easy-to-use Stata and R packages that implement alternative uncertainty measures robust to small G, and (b) explaining and providing evidence for the advantages of these alternatives, especially cluster-adjusted t-statistics based on Ibragimov and Müller. To illustrate these advantages, we reanalyze recent work where results are based on cluster-robust standard errors.

Political science is in large part the study of power, but power itself is difficult to measure. We argue that we can use newspaper coverage—in particular, the relative amount of space devoted to particular subjects in newspapers—to measure the relative power of an important set of political actors and offices. We use a new dataset containing nearly 50 million historical newspaper pages from 2,700 local US newspapers over the years 1877–1977. We define and discuss a measure of power we develop based on observed word frequencies, and we validate it through a series of analyses. Overall, we find that the relative coverage of political actors and of political offices is a strong indicator of political power for the cases we study. To illustrate its usefulness, we then apply the measure to understand when (and where) state party committees lost their power. Taken together, the paper sheds light on the nature of political news coverage and offers both a new dataset and a new measure for studying political power in a wide set of contexts.

Fixed effects estimators are frequently used to limit selection bias. For example, it is well known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable’s effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable’s effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls.

Are the social networks of legislators affected more by their political parties or their personal traits? How does the party organization influence the tendency of members to work collectively on a day-to-day basis? In this paper, I explore the determinants of the relationships of legislators in the Brazilian Chamber of Deputies. I use exponential random graph models to evaluate the relative influence of personal traits versus party influence in generating legislator relationships. Despite a focus on personalism in Brazil, the analysis reveals that the effects of political parties on tie formation are roughly equal to the effects of personal traits, suggesting that networks may make political parties much more cohesive than contemporary literature would lead us to believe.

Across a broad range of fields in political science, there are many theoretically interesting dependent variables that can be characterized as compositions. We build on recent work that has developed strategies for modeling variation in such variables over time by extending them to models of time series cross-sectional data. We discuss how researchers can incorporate the influence of contextual variables and spatial relationships into such models. To demonstrate the utility of our proposed strategies, we present a methodological illustration using an analysis of budgetary expenditures in the US states.

Our focus is a puzzle: that ideological identification as “liberal” is in serious decline in the United States, but at the same time support for liberal policies and for the political party of liberalism is not. We aim to understand this divorce in “liberal” in name and “liberal” in policy by investigating how particular symbols rise and fall as associations with the ideological labels “liberal” and “conservative.” We produce three kinds of evidence to shed light on this macro-level puzzle. First, we explore the words associated with “liberal” and “conservative” over time. Then we take up a group conception by examining the changing correlations between affect toward “liberals” and affect toward other groups. Finally, we consider the changing policy correlates of identification.

China’s rapid rise on the global economic stage has substantial and unequal employment effects in advanced industrialized democracies given China’s large volume of low-wage labor. Thus far, these effects have not been analyzed in the comparative political economy literature. Building on pooled time-series data, we analyze the effects of Chinese trade competition across 17 sectors in 18 countries. We devote attention to a new channel, increased competition from China in foreign export markets. Our empirical findings reveal overall employment declines in sectors more exposed to Chinese imports. Furthermore, our results suggest that employment effects are not equally shared across skill levels, as the share of hours worked worsens for low-skilled workers.

How do personality traits condition the effects of campaign messages meant to mobilize voters? With two nationally representative US survey experiments, I show that common aggressive metaphors mobilize or demobilize voters depending on their traits. Aggressive metaphors increase the mobilizing impact of motivations to participate among aggressive individuals but decrease that impact among low-aggression people. For example, the language mobilizes strong partisans with aggressive personalities but demobilizes strong partisans low in aggression. This heterogeneity showcases the nuanced power of metaphors in campaigns, reaffirms the importance of personality in political behavior, and reveals the hidden role of aggression in non-violent political behavior for the first time. In practice, the net effects of aggressive metaphors can be positive, negative, or null depending on average traits in an audience.

This study uses a discordant sibling-based design to assess the extent to which education is proxying for pre-adult experiences and predispositions rooted in the family. It draws on a unique data set that combines official voting records with Census data on siblings and their parents. The results show that the association between education and voting is considerably reduced when parental education, parental voting and unobserved characteristics that are shared by siblings within the same family are taken into account. This finding is confirmed by a variety of robustness checks. We end with a discussion of the benefits and limitations of sibling-based designs for testing causal hypotheses.

We use two original surveys (including survey experiments) conducted the week before the 2015 elections in the Britain and Denmark to explore the best method for measuring individuals’ knowledge of the partisan distribution of legislative seats in multi-party democracies. The complete lack of such questions in the corpus of survey research on multi-party democracies is a testament to the skepticism that many survey researchers have about the feasibility of such complex questions. However, our analysis, which is the first empirical test of this skepticism, reveals little evidence of respondents’ frustration or high levels of non-cooperation with these questions. Additionally, our survey experiments, which examine the usefulness of different question formats, make it clear that such questions should be framed in terms of the numbers of seats each party holds rather than shares or percentages of seats.

Position change is an essential feature of political competition. Implicitly, policy change on an issue dimension is often equated with opinion change on specific issues within that dimension. However, in addition to opinion-based policy change, we highlight that parties and candidates can change their overall position by increasing their emphasis on certain opinions within that issue dimension (emphasis-based policy change). Using party manifesto data, we find that parties differ in their use of each type of policy change based on aspects of party organization, particularly the relative power of leaders and activists. Leader-dominated parties are more likely to engage in opinion-based policy change, also in reaction to systemic policy shifts. In contrast, activist-dominated parties tend to change their overall position in reaction to systemic shifts by emphasizing certain positions more. Our approach links salience-based to spatial models of party competition and has broader implications for how we study party competition.

Do coders of political texts incorporate prior beliefs about parties’ issue stances into their coding decisions? We report results from a coding experiment in which ten coders were each given 200 statements on immigration that were extracted from election manifestos. Party labels in these statements were randomly assigned (including a control category without party cues). Coders were more likely to code a statement as pro-immigration if it was attributed to the Greens and less likely choose the anti-immigration category if it was attributed to the populist radical right. No effect was found for mainstream parties of the center-left and center-right. The results also suggest that coders resort to party cues as heuristics when faced with ambiguous policy statements.

This manuscript presents a new measure of safety net program effectiveness—a “poverty relief ratio”—that is based on the estimated relationship between market income and social transfers, and reports the amount of income support provided, relative to the amount required to provide for all low-income households’ basic needs. In an important advance over the standard poverty reduction rate measures, the poverty relief ratio preserves the rank order of observations across varying poverty thresholds. In this paper, we introduce this measure and demonstrate its validity by tracking major changes in federal policy and cross-state variation in safety net programs.